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import torch,os,torchvision
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, models, transforms
from PIL import Image
from sklearn.model_selection import StratifiedShuffleSplit
from torchviz import make_dot
from datetime import datetime
from tqdm import tqdm
DATA_ROOT = './data/dog-breed-identification'
from functools import wraps
from time import time
def timing(f):
@wraps(f)
def wrap(*args, **kw):
ts = time()
result = f(*args, **kw)
te = time()
print('func:%r args:[%r, %r] took: %2.4f sec' % \
(f.__name__, args, kw, te-ts))
return result
return wrap
class DogDataset(Dataset):
def __init__(self, labels_df, img_path, transform=None):
self.labels_df = labels_df
self.img_path = img_path
self.transform = transform
def __len__(self):
return self.labels_df.shape[0]
def __getitem__(self, idx):
image_name = os.path.join(self.img_path, self.labels_df.id[idx]) + '.jpg'
img = Image.open(image_name)
label = self.labels_df.label_idx[idx]
if self.transform:
img = self.transform(img)
return img, label
@timing
def train(model,device, train_loader, epoch):
model.train()
for batch_idx, data in tqdm(enumerate(train_loader)):
print('{}: {}/{}, {}'.format(epoch, batch_idx, len(train_dataset), data[0].shape))
x,y= data
x=x.to(device)
y=y.to(device)
optimizer.zero_grad()
y_hat= model(x)
loss = criterion(y_hat, y)
loss.backward()
optimizer.step()
print ('Train Epoch: {}\t Loss: {:.6f}'.format(epoch,loss.item()))
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for i,data in enumerate(test_loader):
x,y= data
x=x.to(device)
y=y.to(device)
optimizer.zero_grad()
y_hat = model(x)
test_loss += criterion(y_hat, y).item() # sum up batch loss
pred = y_hat.max(1, keepdim=True)[1] # get the index of the max log-probability
correct += pred.eq(y.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(val_dataset),
100. * correct / len(val_dataset)))
if __name__ == '__main__':
all_labels_df = pd.read_csv(os.path.join(DATA_ROOT, 'labels.csv'))
breeds = all_labels_df.breed.unique()
breed2idx = dict((breed, idx) for idx, breed in enumerate(breeds))
idx2breed = dict((idx, breed) for idx, breed in enumerate(breeds))
# all_labels_df['label_idx'] = [breed2idx[b] for b in all_labels_df.breed]
all_labels_df['label_idx'] = all_labels_df['breed'].apply(lambda x: breed2idx[x])
print(all_labels_df.head())
model_ft = models.resnet50(pretrained=True)
models.resnet18()
print(model_ft)
# make_dot(yhat, params=dict(list(model_ft.named_parameters()))).render("resnet50_torchviz", format="png")
IMG_SIZE = 224 # resnet50的输入是224的所以需要将图片统一大小
BATCH_SIZE = 256 # 这个批次大小需要占用4.6-5g的显存，如果不够的化可以改下批次，如果内存超过10G可以改为512
IMG_MEAN = [0.485, 0.456, 0.406]
IMG_STD = [0.229, 0.224, 0.225]
CUDA = torch.cuda.is_available()
DEVICE = torch.device("cuda" if CUDA else "cpu")
train_transforms = transforms.Compose([
transforms.Resize(IMG_SIZE),
transforms.RandomResizedCrop(IMG_SIZE),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(30),
transforms.ToTensor(),
transforms.Normalize(IMG_MEAN, IMG_STD)
])
val_transforms = transforms.Compose([
transforms.Resize(IMG_SIZE),
transforms.CenterCrop(IMG_SIZE),
transforms.ToTensor(),
transforms.Normalize(IMG_MEAN, IMG_STD)
])
dataset_names = ['train', 'valid']
stratified_split = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=0)
train_split_idx, val_split_idx = next(iter(stratified_split.split(all_labels_df.id, all_labels_df.breed)))
train_df = all_labels_df.iloc[train_split_idx].reset_index()
val_df = all_labels_df.iloc[val_split_idx].reset_index()
# print(len(train_df))
# print(len(val_df))
image_transforms = {'train': train_transforms, 'valid': val_transforms}
train_dataset = DogDataset(train_df, os.path.join(DATA_ROOT, 'train'), transform=image_transforms['train'])
val_dataset = DogDataset(val_df, os.path.join(DATA_ROOT, 'train'), transform=image_transforms['valid'])
image_dataset = {'train': train_dataset, 'valid': val_dataset}
image_dataloader = {x: DataLoader(image_dataset[x], batch_size=BATCH_SIZE, shuffle=True, num_workers=0) for x in
dataset_names}
dataset_sizes = {x: len(image_dataset[x]) for x in dataset_names}
# batch = next(iter(train_dataset))
# yhat = model_ft(batch[0].unsqueeze(0))
# make_dot(yhat).render("resnet_ft")
# 将所有的参数层进行冻结
for param in model_ft.parameters():
param.requires_grad = False
# 这里打印下全连接层的信息
print(model_ft.fc)
num_fc_ftr = model_ft.fc.in_features # 获取到fc层的输入
model_ft.fc = nn.Linear(num_fc_ftr, len(breeds)) # 定义一个新的FC层
model_ft = model_ft.to(DEVICE) # 放到设备中
print(model_ft) # 最后再打印一下新的模型
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam([
{'params': model_ft.fc.parameters()}
], lr=0.001) # 指定 新加的fc层的学习率
model_parameters = filter(lambda p: p.requires_grad, model_ft.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print('params: {}, start time: {}'.format(params, datetime.now()))
for epoch in range(1, 10):
train(model=model_ft, device=DEVICE, train_loader=image_dataloader["train"], epoch=epoch)
test(model=model_ft, device=DEVICE, test_loader=image_dataloader["valid"])
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